Nowadays, energy systems more and more exchange data such as meter readings. In the future, also data that reflects the energy demand of e.g. individual appliances or households will be used, especially in so-called smart grids. This data is typically sent upstream in a tree-like network. At a next node in the network, the data is typically aggregated for several reasons, for example because the aggregated value is typically the most relevant and interesting to the nodes higher in the network, because concentrating the information improves scalability and because the level of privacy for the nodes lower in the network can be increased by aggregating the data.
FIG. 1 illustrates an example of an energy network with a tree topology. In particular, it illustrates a typical network structure for an Advanced Metering Infrastructure (AMI). The energy network 100 comprises a network aggregator 101 at the highest level of the network tree or hierarchy, city aggregators 102, 103, 104 at one level below the network aggregator 101, and neighborhood aggregators 105, 106, 107 at one level below the city aggregators 102, 103, 104. In operation, a first neighborhood aggregator 105 aggregates energy data received from a first plurality of energy meters 108, 109, 110. A second neighborhood aggregator 106 aggregates energy data received from a second plurality of energy meters 111, 112, 113. A third neighborhood aggregator 107 aggregates energy data received from a third plurality of energy meters 114, 115, 116. The neighborhood aggregators 105, 106, 107 send their aggregated energy data to city aggregator 103, which in turn performs another aggregation of data and sends its aggregated data to the network aggregator 101.
FIG. 2 illustrates an example of a system architecture 200 for an energy network of the future. Energy networks of the future, which are often referred to as smart grids, will probably exchange even more data than present-day energy networks. Furthermore, these data will typically be sensitive in nature. The nodes in such a network are typically devices such as white goods, electric vehicles, photovoltaic (PV) panels, e-meters, home energy gateways etc. An example of a system for such future networks is the so-called “PowerMatcher” system (disclosed on the website http://www.powermatcher.net/).
The “PowerMatcher” system 200 aims at balancing the energy demands (consumption side) and offerings (production side) in a distributed energy system. This balancing is performed as follows: devices send bids upstream, that reflect their current energy demand (positive price, showing what they want to pay) and/or their energy offering (negative price, showing for what price they want to deliver energy). The next upstream node aggregates the bids of the different devices and sends this aggregated bid up to the next node in the tree. This mechanism is continued up to the top level node, i.e. the so-called “Auctioneer”. This node has an overview of the total energy demand and supply in the network. Based on this overview, it determines a market equilibrium in consumption and production (and the associated “price”), which is sent downstream to the different devices. The devices can then adjust their actual energy consumption based on the new market price.
Energy networks of the future may have the disadvantage that a large amount of detailed, personal data is available. The exchange and processing of such data in a network comprising unprotected network nodes poses a large privacy problem. There are many research papers that show, for example, that it is quite easy to deduce a lot of information about a household (e.g. the number of rooms, the number of people inside the home, the kind of devices that they have and use, which TV channel they watch etc.) merely by reading the instantaneous consumption data, as can be done in an Advanced Metering Infrastructure (AMI).
Furthermore, energy networks of the future may have the disadvantage that for certain types of data the exposure of said data could allow third parties to profit at the expense of the data-sending party or parties. An example of such a situation is when the data contains e.g. a bid on a certain commodity; if exposed to another party or parties that party or those parties could take the original bid into account when formulating their bid—thereby “gaming” the system.